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GATE DA Question Papers with Solutions 2026 - IIT Guwahati conducted the GATE 2026 DA exam on February 15, 2026, in online mode. Candidates who attempted the GATE DA 2026 paper can now check the officially released Master Question Paper, Candidate Response, and Master Answer Key available on the GOAPS portal by Indian Institute of Technology Guwahati, along with detailed exam analysis provided in this article. These are memory-based questions and allow candidates to determine their performance in the GATE exam. The unofficial GATE 2026 questions have been updated here. Hence, candidates can review these questions to know the difficulty level of the paper.
This article provides the direct link to download the GATE Data Science and Artificial Intelligence Question Papers with Answers PDF. The Subject-Wise weightage and the detailed exam pattern for the GATE 2026 Exam can also be found in this article below. Solving the GATE 2026 DA question paper plays a very important role in the preparation. Doing this helps candidates become familiar with the GATE 2026 Exam. Whether you are reviewing your performance or preparing for the next cycle, having access to the GATE 2026 DA Question Paper With Solutions is the first step toward strategic preparation. The GATE 2026 results were officially announced today on the official website of IIT Guwahati.
The GATE DA question paper is designed to check a candidate's knowledge in data science and AI. Unlike the general engineering papers, the DA paper requires a strong knowledge of mathematical concepts and programming logic. Understanding the structure is important before you attempt the GATE 2026 DA Question Paper. For better understanding, you can refer to the GATE 2026 Exam Pattern. Find the exam details below:
| Paper Name | Data Science and Artificial Intelligence |
| Paper Code | DA |
| Conducting Body | IIT Guwahati |
| Mode of Examination | Computer-Based |
| Total Marks | 100 |
| Duration | 3 Hours |
| Section-Wise Weightage | General Aptitude: 15 Marks Subject Questions: 85 Marks |
| Question Types | MCQs, MSQs, NATs |
Types of Questions | Number of questions |
Multiple choice questions(MCQs) | 33 |
Multiple select questions(MSQs) | 13 |
Numerical type questions(NATs) | 19 |
Memory-Based Question:
Question 1: Which of the following is not an uninformed search?
(a)DFS
(b)BFS
(c)A*
(d)DLS
Question 2: If xyz is a three-digit number and the product of its digits is 70, then find the sum of the digits.
(a) 16
(b) 14
(c) 18
(d) 12
Question 3:Consider the ER model:
E1 (A11, A12, A13)
E2 (A21, A22, A23)
Where:
- A21 is the key of E2
- A22 is a multivalued attribute
- R12 is a many-to-many relationship between E1 and E2
Find the minimum number of relations required to convert this ER model into tables.
Question 4: Consider a binary tree whose preorder traversal is
P, Q, S, E, R, F, G
And inorder traversal is
S, Q, E, P, F, R, G
Which of the followinng statement is correct?
(a)Node Q has only one child
(b)Postorder traversal is SEQFGRP
(c)P is the root of the tree
(d)The left subtree of node R contains node G
Question 5: In a data warehouse, a 3D cube initially has dimensions: Product type, Month, and Country. After visualization, the dimension Country is refined into State. Which OLAP operation is performed?
(a) Slicing
(b) Dicing
(c) Rollup
(d) drill down
Question 6: Find the maximum number of node pointers that can be used, given:
Node size = 4096 B Node pointer = 10 B Search key = 11 B Record pointer = 12 B
Question 7: Given the Python code:
def fun(L, i=0):
if i >= len(L) - 1:
return 0
if L[i] > L[i+1]:
L[i+1], L[i] = L[i], L[i+1]
L[i+1], L[i] = L[i], L[i+1] )
else:
return fun(L, i+1)
data = [5, 3, 4, 1, 2]
count = 0
for _ in range(len(data)):
count += fun(data)
print(count)
Question 8: Consider two relations R(A, B) and S(E, C). A is the primary key, and E is a FK referring to A. Which of the following operations never violate FK constraint?
(a) Insert in R
(b) Delete from S
(c) Delete from R
(d) Insert in S
Below we have provided the link to the GATE 2026 official question paper:
You don’t need to invest equal time on every subject. Some subjects, like Probability and Machine Learning, are high-scoring, while others might take a lot of your time for only a few marks. Knowing the weightage helps you decide which subject to prioritize. For a better understanding of the topics, you can refer to the detailed GATE 2026 DA Syllabus.
Chapter Name | 2025 | 2024 | Total | Percentage Distribution |
Aptitude | 10 | 10 | 20 | 15.38% |
Artificial Intelligence(AI) | 4 | 7 | 11 | 8.46% |
Calculus and Optimization | 6 | 5 | 11 | 8.46% |
Database Management and Warehousing | 7 | 4 | 11 | 8.46% |
Linear Algebra | 8 | 6 | 14 | 10.77% |
Machine Learning | 9 | 10 | 19 | 14.62% |
Probability and Statistics | 12 | 10 | 22 | 16.92% |
Programming, Data Structures, and Algorithms | 9 | 13 | 22 | 16.92% |
Total | 65 | 65 | 130 | 100.00% |
The GATE DA 2026 Syllabus is very vast; it is very difficult to master every topic. So instead of going the hard way, we will go the smart way. The secret to getting a high rank lies in identifying the High-Weightage topics. Here are some of the most important topics for GATE DA 2026:
Chapter Name | Topic Name | Count |
Aptitude | Dice folding and visualization | 2 |
Geometry – cross-section visualization | 2 | |
Graph coloring (minimum colors) | 2 | |
Inference from the passage | 2 | |
Infinite series sum | 2 | |
Permutations – divisibility | 1 | |
Permutations – Divisibility rule | 1 | |
Pie chart – percentage calculation | 2 | |
Probability of combinations (girls/boys) | 1 | |
Profit/Interest calculation (returns) | 2 | |
Verbal analogy | 2 | |
Aptitude Total | 19 | |
Artificial Intelligence | AI – Adversarial search (alpha-beta pruning) | 1 |
AI – Heuristic admissibility (h1, h2) | 1 | |
AI – Search strategy (A*) and heuristic admissibility | 1 | |
Alpha-beta pruning in adversarial search | 1 | |
Bayesian network – conditional independence | 1 | |
Bayesian network – joint probability computation | 1 | |
BFS vs DFS – state expansion count | 1 | |
Logic representation – rugby and round balls | 1 | |
Neural network – weight equivalence | 1 | |
Propositional logic – tautology identification | 1 | |
Artificial Intelligence | 11 | |
Calculus and Optimization | Function continuity and differentiability (piecewise) | 1 |
Limits and logarithmic expansion | 1 | |
Limits and logarithmic expansions | 1 | |
Local maxima/minima) | 1 | |
Local maxima/minima of a quartic polynomial | 1 | |
Logistic function derivative (0.4 value) | 1 | |
Optimization – function continuity and differentiability | 1 | |
Optimization – local minima (2nd derivative test) | 2 | |
Optimization – Taylor series and limits | 1 | |
Calculus and Optimization Total | 10 | |
Database Management and Warehousing | ER model – relational schema (DB constraints) | 1 |
Functional dependencies (DB) | 1 | |
Functional dependencies (derivable attributes) | 1 | |
Normalization & z-score | 1 | |
Relational algebra – ensuring team members in defender/forward | 1 | |
Relational algebra – set operations (Team/Defender) | 1 | |
SQL – Index optimization (hash vs B+) | 1 | |
SQL indexing optimization (hash vs B+) | 1 | |
SQL query result count (joins with conditions) | 1 | |
Database Management and Warehousing Total | 9 | |
Linear Algebra | Determinant of M2+12MM^2+12M | 1 |
Eigenvalues and matrix properties | 1 | |
Eigenvalues and signs of matrix | 1 | |
Eigenvalues of matrices | 1 | |
Eigenvalues, determinant, and matrix property | 1 | |
Matrix rank and nullity (subspaces) | 1 | |
Matrix solution scenarios (unique/infinite/none) | 1 | |
Matrix solutions (unique/infinite/no solutions) | 1 | |
Projection matrix properties | 2 | |
Python recursion & tree traversal | 1 | |
Singular values and sum | 1 | |
Singular values and their sum (SVD) | 1 | |
Subspaces of R3R^3 | 1 | |
Subspaces of R3R^3R3 | 1 | |
Vector subspace properties | 1 | |
Linear Algebra Total | 16 | |
Machine Learning | Clustering – single linkage algorithm | 2 |
Clustering – single linkage algorithm | 2 | |
Fisher Linear Discriminant (between/within scatter matrices) | 1 | |
k-means clustering – point assignment | 1 | |
k-means clustering properties | 2 | |
k-NN classifier (minimum k for classification) | 1 | |
ML – Linear separability (2D datasets) | 1 | |
ML – Linear separability of datasets | 3 | |
Naive Bayes – number of parameters estimation | 1 | |
Neural network – weight equivalence (ReLU) | 1 | |
PCA, Naive Bayes, Logistic regression (classification of models) | 1 | |
SVM – support vectors | 1 | |
Machine Learning Total | 17 | |
Probability and Statistics | Binary search recurrence relation | 1 |
Covariance between random variables | 1 | |
Dynamic programming (prefix computation) | 1 | |
Expected throws until two consecutive even outcomes | 1 | |
Logic – Propositional representation (balls/rugby) | 1 | |
Poisson distribution & Normal distribution properties | 2 | |
Probability – Bayes theorem | 2 | |
Probability – conditional expectation and variance | 1 | |
Probability – conditional/joint events | 3 | |
Probability – event intersection (T ∩ S) | 1 | |
Probability – expected value (die throws) | 1 | |
Probability – exponential distribution parameter | 2 | |
Probability – joint PDF and expectation | 2 | |
Probability – uniform distribution (X, Y) | 1 | |
Probability – uniform distributions | 1 | |
Probability – z-score normalization | 1 | |
Probability of combinations (girls/boys) | 1 | |
Python list reverse (recursion) | 1 | |
Sample mean update with new data | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Probability and Statistics Total | 26 | |
AI – Heuristic admissibility (h1, h2) | 1 | |
Array prefix computation (dynamic programming) | 1 | |
Bayesian network joint probability | 1 | |
Binary search comparisons recurrence | 1 | |
Binary search complexity analysis | 1 | |
Binary tree node relationships (height, leaves) | 1 | |
Binary tree properties (height, nodes) | 1 | |
Covariance between random variables | 1 | |
DFS edge classification (tree/cross/back) | 2 | |
Double-ended queue operations (insert/remove) | 1 | |
k-NN classifier (minimum k for classification) | 1 | |
Python list reverse using recursion | 1 | |
Python recursion – counting tree nodes | 1 | |
Quicksort – swaps count | 1 | |
Relational algebra – SQL tuple verification | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Topological sort of DAG | 1 | |
Topological sorting in DAG | 1 | |
Tree traversal combinations (preorder/inorder/postorder) | 1 | |
Uniform hashing – expected probes | 1 | |
Programming, Data Structures, and Algorithms Total | 22 | |
Grand Total | 130 |
Candidates can find the previous year's GATE DA question papers in the table below.
IIT Guwahati has release the official GATE DA Question Paper. Candidates can follow the steps to download the question paper
Log on to the GATE 2026 official website.
Go to the “Downloads” section.
Click the GATE 2026 DA Question Paper with Solutions PDF download link.
Download the pdf and keep practicing.
Practicing with previous years' papers plays a very important role in your preparation. To score a top rank in GATE 2026, you have to combine these papers with GATE Mock Tests 2026. Here is how focusing on the GATE 2026 DA Question Paper with Solutions PDF Free Download can be a game-changer for you:
Understand the Latest Trend: AI is not static. If you look at the 2024 vs. 2025 papers, you'll see a clear shift toward practical Machine Learning from pure theory.
Time Management: Three hours sounds like a long time until you're stuck on a complex DBMS query. Mocks teach you when to skip and when to commit.
Self-Assessment: You can easily analyse your weak areas in topics like Machine Learning and DBMS when you solve the GATE Data Science and Artificial Intelligence Question Papers with Answers PDF.
Frequently Asked Questions (FAQs)
No, there is no age limit to appear for the exam.
Yes, there is negative marking in the GATE exam. Marking Scheme GATE 2026: Here, you will get the detailed information about the marking scheme in the GATE exam.
The GATE Data Science and AI syllabus 2026 includes subjects such as Probability, Statistics, linear algebra, Algorithm, Programming, Data Structures, DBMS, and Machine learning. Please refer to the GATE DA Syllabus 2026 for a deeper understanding.
There will be three types of questions in GATE DA 2026, Multiple choice questions(MCQs), multiple-selection questions(MSQs), and numerical-answer-type questions(NATs).
On Question asked by student community
Hi Atihse,
You have got a very good score and rank. Based on previous years trends, With GATEn score of 423 you are expected to get colleges like MNIT Allahabad, VNIT Nagpur, NIT Rourkela, IIEST Shibpur, Howrah. Also have a look into the below link.
Link: https://engineering.careers360.com/articles/gate-rank-vs-college
Hello,
You can find information about Mtech CS admission for 2026 without GATE qualification marks through the link provided below.
https://engineering.careers360.com/articles/list-of-mtech-colleges-without-gate-score
Any BTech ECE graduate can apply for all the NITs offering MTech CSE based on GATE exam. Some mid and lower NITs are:
You can check the complete list of NITs offers MTech CSE are - https://engineering.careers360.com/colleges/list-of-top-nit-colleges-in-india
Hi Shipra,
To get GATE rank of 1026 in Biotechnology, you need to score GATE marks around 75 out of 100. Check the GATE marks vs Rank article link below to understand it clearly.
Link: https://engineering.careers360.com/articles/gate-marks-vs-rank
With a qualifying GATE 2026 score of 32 under the EWS category, candidates will likely secure MTech seats in lower-demand branches in newer NITs/IIITs/GFTIs.
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